Spatial-Frequency Characteristics of EEG Associated with the Mental Stress in Human-Machine Systems.

Accurate assessment of user mental stress in human-machine system plays a crucial role in ensuring task performance and system safety. However, the underlying neural mechanisms of stress in human-machine tasks and assessment methods based on physiological indicators remain fundamental challenges. In this paper, we employ a virtual unmanned aerial vehicle (UAV) control experiment to explore the reorganization of functional brain network patterns under stress conditions. The results indicate enhanced functional connectivity in the frontal theta band and central beta band, as well as reduced functional connectivity in the left parieto-occipital alpha band, which is associated with increased mental stress. Evaluation of network metrics reveals that decreased global efficiency in the theta and beta bands is linked to elevated stress levels. Subsequently, inspired by the frequency-specific patterns in the stress brain network, a cross-band graph convolutional network (CBGCN) model is constructed for mental stress brain state recognition. The proposed method captures the spatial-frequency topological relationships of cross-band brain networks through multiple branches, with the aim of integrating complex dynamic patterns hidden in the brain network and learning discriminative cognitive features. Experimental results demonstrate that the neuro-inspired CBGCN model improves classification performance and enhances model interpretability. The study suggests that the proposed approach provides a potentially viable solution for recognizing stress states in human-machine system by using EEG signals.